{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('diabetes.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同类的数量\n",
      "0    500\n",
      "1    268\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "print('不同类的数量')\n",
    "print(data['Outcome'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据质量分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输入属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.Pregnancies.values, bins=40, kde=False)\n",
    "plt.xlabel('Pregnancies', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.Glucose.values, bins=30, kde=False)\n",
    "plt.xlabel('Glucose', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.BloodPressure.values, bins=30, kde=False)\n",
    "plt.xlabel('BloodPressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.SkinThickness.values, bins=40, kde=False)\n",
    "plt.xlabel('SkinThickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAENCAYAAAABh67pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAEwBJREFUeJzt3X/sXfV93/Hnq5iSNEQByneea5uZtl4jGi0GuUCWasugaQBVM5VSCo0SL6JyMxEt2aJt0P3RRBpSOqVhQepQnZLG6ZIQmqTFoqwpdaiq/BETk7oO5sfyTYBgz+BvEyA/0NAg7/1xP4Yb1/5+7/d779eX78fPh3R1z/mczznncz86ft3z/ficc1NVSJL69WPTboAkaXkZ9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOrZp2AwDOPvvs2rBhw7SbIUkryn333ff3VTWzUL2XRdBv2LCBPXv2TLsZkrSiJHlslHoO3UhS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUudeFnfGjuNTu7/1I/O/cdE5U2qJJL08eUYvSZ0z6CWpcwa9JHXOoJekzhn0ktQ5g16SOmfQS1LnDHpJ6tyCQZ/kFUnuTfJ3SfYn+UAr/3iSR5Lsba9NrTxJbk4ym2RfkguW+0NIko5vlDtjnwMuqarvJzkV+FKS/9WW/ceq+uxR9S8HNrbXRcAt7V2SNAULntHXwPfb7KntVfOssgX4RFvvy8AZSdaM31RJ0lKMNEaf5JQke4HDwN1VtbsturENz9yU5LRWthZ4fGj1A61MkjQFIwV9Vb1QVZuAdcCFSV4H3AC8FvgF4CzgPy9mx0m2JdmTZM/c3Nwimy1JGtWirrqpqqeBe4DLqupQG555Dvgj4MJW7SCwfmi1da3s6G1tr6rNVbV5ZmZmaa2XJC1olKtuZpKc0aZfCbwZeOjIuHuSAFcC97dVdgLvaFffXAw8U1WHlqX1kqQFjXLVzRpgR5JTGHwx3F5Vdyb5YpIZIMBe4F2t/l3AFcAs8Czwzsk3W5I0qgWDvqr2Aecfo/yS49Qv4LrxmyZJmgTvjJWkzhn0ktQ5g16SOmfQS1LnDHpJ6pxBL0mdM+glqXMGvSR1zqCXpM4Z9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6t2DQJ3lFknuT/F2S/Uk+0MrPTbI7yWySzyT58VZ+Wpufbcs3LO9HkCTNZ5Qz+ueAS6rq9cAm4LIkFwO/C9xUVT8LPAVc2+pfCzzVym9q9SRJU7Jg0NfA99vsqe1VwCXAZ1v5DuDKNr2lzdOWX5okE2uxJGlRRhqjT3JKkr3AYeBu4BvA01X1fKtyAFjbptcCjwO05c8APznJRkuSRjdS0FfVC1W1CVgHXAi8dtwdJ9mWZE+SPXNzc+NuTpJ0HIu66qaqngbuAd4AnJFkVVu0DjjYpg8C6wHa8tcA3z7GtrZX1eaq2jwzM7PE5kuSFjLKVTczSc5o068E3gw8yCDw39qqbQXuaNM72zxt+RerqibZaEnS6FYtXIU1wI4kpzD4Yri9qu5M8gBwW5L/CvwtcGurfyvwx0lmge8AVy9DuyVJI1ow6KtqH3D+Mcq/yWC8/ujy/wv82kRaJ0kam3fGSlLnDHpJ6pxBL0mdM+glqXMGvSR1zqCXpM4Z9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ1bMOiTrE9yT5IHkuxP8p5W/v4kB5Psba8rhta5IclskoeTvGU5P4AkaX4L/jg48Dzwvqr6apJXA/clubstu6mqPjRcOcl5wNXAzwM/BfxVkn9aVS9MsuGSpNEseEZfVYeq6qtt+nvAg8DaeVbZAtxWVc9V1SPALHDhJBorSVq8RY3RJ9kAnA/sbkXvTrIvyceSnNnK1gKPD612gGN8MSTZlmRPkj1zc3OLbrgkaTQjB32S04HPAe+tqu8CtwA/A2wCDgG/t5gdV9X2qtpcVZtnZmYWs6okaRFGCvokpzII+U9W1ecBqurJqnqhqn4IfJSXhmcOAuuHVl/XyiRJUzDKVTcBbgUerKoPD5WvGar2q8D9bXoncHWS05KcC2wE7p1ckyVJizHKVTdvBN4OfC3J3lb228A1STYBBTwK/BZAVe1PcjvwAIMrdq7zihtJmp4Fg76qvgTkGIvummedG4Ebx2iXJGlCvDNWkjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzhn0ktQ5g16SOmfQS1LnRvlx8PVJ7knyQJL9Sd7Tys9KcneSr7f3M1t5ktycZDbJviQXLPeHkCQd3yhn9M8D76uq84CLgeuSnAdcD+yqqo3ArjYPcDmwsb22AbdMvNWSpJEtGPRVdaiqvtqmvwc8CKwFtgA7WrUdwJVtegvwiRr4MnBGkjUTb7kkaSSLGqNPsgE4H9gNrK6qQ23RE8DqNr0WeHxotQOtTJI0BSMHfZLTgc8B762q7w4vq6oCajE7TrItyZ4ke+bm5hazqiRpEUYK+iSnMgj5T1bV51vxk0eGZNr74VZ+EFg/tPq6VvYjqmp7VW2uqs0zMzNLbb8kaQGjXHUT4Fbgwar68NCincDWNr0VuGOo/B3t6puLgWeGhngkSSfYqhHqvBF4O/C1JHtb2W8DHwRuT3It8BhwVVt2F3AFMAs8C7xzoi2WJC3KgkFfVV8CcpzFlx6jfgHXjdkuSdKEeGesJHXOoJekzhn0ktQ5g16SOmfQS1LnDHpJ6pxBL0mdM+glqXMGvSR1zqCXpM4Z9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1LkFgz7Jx5IcTnL/UNn7kxxMsre9rhhadkOS2SQPJ3nLcjVckjSaUc7oPw5cdozym6pqU3vdBZDkPOBq4OfbOv8jySmTaqwkafEWDPqq+hvgOyNubwtwW1U9V1WPALPAhWO0T5I0pnHG6N+dZF8b2jmzla0FHh+qc6CVSZKmZKlBfwvwM8Am4BDwe4vdQJJtSfYk2TM3N7fEZkiSFrKkoK+qJ6vqhar6IfBRXhqeOQisH6q6rpUdaxvbq2pzVW2emZlZSjMkSSNYUtAnWTM0+6vAkStydgJXJzktybnARuDe8ZooSRrHqoUqJPk08Cbg7CQHgN8B3pRkE1DAo8BvAVTV/iS3Aw8AzwPXVdULy9N0SdIoFgz6qrrmGMW3zlP/RuDGcRolSZoc74yVpM4Z9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzhn0ktQ5g16SOrdg0Cf5WJLDSe4fKjsryd1Jvt7ez2zlSXJzktkk+5JcsJyNlyQtbJQz+o8Dlx1Vdj2wq6o2ArvaPMDlwMb22gbcMplmSpKWasGgr6q/Ab5zVPEWYEeb3gFcOVT+iRr4MnBGkjWTaqwkafGWOka/uqoOtekngNVtei3w+FC9A63sH0iyLcmeJHvm5uaW2AxJ0kLG/s/YqiqglrDe9qraXFWbZ2Zmxm2GJOk4lhr0Tx4Zkmnvh1v5QWD9UL11rUySNCVLDfqdwNY2vRW4Y6j8He3qm4uBZ4aGeCRJU7BqoQpJPg28CTg7yQHgd4APArcnuRZ4DLiqVb8LuAKYBZ4F3rkMbZYkLcKCQV9V1xxn0aXHqFvAdeM2SpI0Od4ZK0mdM+glqXMGvSR1zqCXpM4Z9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ0z6CWpcwv+lOB8kjwKfA94AXi+qjYnOQv4DLABeBS4qqqeGq+ZkqSlmsQZ/b+qqk1VtbnNXw/sqqqNwK42L0makuUYutkC7GjTO4Arl2EfkqQRjRv0BfxlkvuSbGtlq6vqUJt+Alg95j4kSWMYa4we+MWqOpjkHwF3J3loeGFVVZI61orti2EbwDnnnDNmMyRJxzPWGX1VHWzvh4E/BS4EnkyyBqC9Hz7OuturanNVbZ6ZmRmnGZKkeSw56JO8Ksmrj0wDvwzcD+wEtrZqW4E7xm2kJGnpxhm6WQ38aZIj2/lUVf1Fkq8Atye5FngMuGr8ZkqSlmrJQV9V3wRef4zybwOXjtMoSdLkeGesJHXOoJekzhn0ktQ5g16SOmfQS1LnDHpJ6pxBL0mdM+glqXPjPtSsC5/a/a0fmf+Ni3zImqR+eEYvSZ3zjH4Bw2f7nulLWokM+mVy9HDQEX5ZSDrRDPpFmG8s/3jBvphtSNJyMOjHMGq4j7oNQ1/ScjDoj2ESAT5pfiFIWiqDvgN+CUiaz0kb9C/Hs/ajLaWNhr6ko520Qf9ytNT/0F3K9v0SkE4e3jAlSZ1btjP6JJcBHwFOAf6wqj64XPsa1UoYrpmklfZ5vfRUWh7LEvRJTgF+H3gzcAD4SpKdVfXAcuxvmMMTS7OUfpvvi8S+l14+luuM/kJgtqq+CZDkNmALsOxBP2ylndGeSPP1zSQC3C+B45v0yYh/CWkhyxX0a4HHh+YPABct0750Ak36JrGl1hv1ruRJ1DvesvkCdRJ3Si/3X6fH2/40vzim9ZlPpGn0b6pq8htN3gpcVlW/2ebfDlxUVe8eqrMN2NZmfw54eIm7Oxv4+zGa2zv7Z372z/zsn/lNu3/+SVXNLFRpuc7oDwLrh+bXtbIXVdV2YPu4O0qyp6o2j7udXtk/87N/5mf/zG+l9M9yXV75FWBjknOT/DhwNbBzmfYlSZrHspzRV9XzSd4NfIHB5ZUfq6r9y7EvSdL8lu06+qq6C7hrubY/ZOzhn87ZP/Ozf+Zn/8xvRfTPsvxnrCTp5cNHIEhS51Z00Ce5LMnDSWaTXD/t9kxDkvVJ7knyQJL9Sd7Tys9KcneSr7f3M1t5ktzc+mxfkgum+wmWX5JTkvxtkjvb/LlJdrc++Ey7YIAkp7X52bZ8wzTbfaIkOSPJZ5M8lOTBJG/w+BlI8u/bv6v7k3w6yStW4vGzYoN+6DELlwPnAdckOW+6rZqK54H3VdV5wMXAda0frgd2VdVGYFebh0F/bWyvbcAtJ77JJ9x7gAeH5n8XuKmqfhZ4Cri2lV8LPNXKb2r1TgYfAf6iql4LvJ5BX530x0+StcC/AzZX1esYXFhyNSvx+KmqFfkC3gB8YWj+BuCGabdr2i/gDgbPGHoYWNPK1gAPt+k/AK4Zqv9ivR5fDO7h2AVcAtwJhMENLquOPo4YXCX2hja9qtXLtD/DMvfPa4BHjv6cHj8FL93hf1Y7Hu4E3rISj58Ve0bPsR+zsHZKbXlZaH8qng/sBlZX1aG26AlgdZs+2frtvwP/Cfhhm/9J4Omqer7ND3/+F/umLX+m1e/ZucAc8EdteOsPk7wKjx+q6iDwIeBbwCEGx8N9rMDjZyUHvYYkOR34HPDeqvru8LIanGKcdJdXJfkV4HBV3TfttryMrQIuAG6pqvOBH/DSMA1wUh8/ZzJ4GOO5wE8BrwIum2qjlmglB/2Cj1k4WSQ5lUHIf7KqPt+Kn0yypi1fAxxu5SdTv70R+NdJHgVuYzB88xHgjCRH7iEZ/vwv9k1b/hrg2yeywVNwADhQVbvb/GcZBL/HD/wS8EhVzVXV/wM+z+CYWnHHz0oOeh+zwOAqCOBW4MGq+vDQop3A1ja9lcHY/ZHyd7SrJy4Gnhn6E70rVXVDVa2rqg0Mjo8vVtXbgHuAt7ZqR/fNkT57a6vf9ZlsVT0BPJ7k51rRpQweJ37SHz8MhmwuTvIT7d/Zkb5ZecfPtP+TYMz/LLkC+N/AN4D/Mu32TKkPfpHBn9X7gL3tdQWDscFdwNeBvwLOavXD4GqlbwBfY3BFwdQ/xwnopzcBd7bpnwbuBWaBPwFOa+WvaPOzbflPT7vdJ6hvNgF72jH0Z8CZHj8v9s0HgIeA+4E/Bk5bicePd8ZKUudW8tCNJGkEBr0kdc6gl6TOGfSS1DmDXpI6Z9BLS5Dkr5P8Zpt+W5K/nHabpOMx6NWlJI8m+aUTsa+q+mRV/fKJ2Je0FAa9JHXOoFfXkvybJF9K8qEkTyV5JMnlRy3/ZpLvtWVva+XvT/I/h+ptSFJDzzj5B/sYmq8k72o/2vF0kt9vt9BLU2HQ62RwEYPnpp8N/Dfg1vasllcBNwOXV9WrgX/O4BESk/ArwC8A/wy4isFzzKWpMOh1Mnisqj5aVS8AOxj8kMaR56v/EHhdkldW1aGq2j+hfX6wqp6uqm8xeAjWpgltV1o0g14ngyeOTFTVs23y9Kr6AfDrwLuAQ0n+PMlrJ71P4Fng9AltV1o0g14ntar6QlW9mcFZ/kPAR9uiHwA/MVT1H5/otkmTYtDrpJVkdZItbaz+OeD7vPSTg3uBf5HknCSvYfCbxNKKZNDrZPZjwH8A/g/wHeBfAv8WoKruBj7D4Bnt9zH4YWhpRfJ59JLUOc/oJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ0z6CWpc/8fkf0HeGOHCZ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.Insulin.values, bins=90, kde=False)\n",
    "plt.xlabel('Insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.BMI.values, bins=30, kde=False)\n",
    "plt.xlabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAENCAYAAAABh67pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFJZJREFUeJzt3X2wZVV95vHvAw0xoOHF7kHemiZCkiFWjKQVLEyGEjNDGLWZBBmEKFoooxNHnSQTmUwMTGJKnMpoMDpxKDA24AsEHUGKZIKIsahIx0aJ8hLHHoeXhkaa8KoYFf3NH3tfOFzO7XvuOffe0736+6m61fvsvc7e66x7+rnrrH322qkqJEnt2mXaFZAkLS2DXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS4FdOuAMDKlStrzZo1066GJO1QbrzxxvuratV85baLoF+zZg0bN26cdjUkaYeS5I5Ryjl0I0mNM+glqXEGvSQ1bt6gT/LhJPcluXlg3b5Jrknyjf7fffr1SfL+JJuSfDXJkUtZeUnS/Ebp0X8EOH7WurOAa6vqcODa/jHArwCH9z9nAn+2ONWUJI1r3qCvqi8AD8xavQ5Y3y+vB04cWH9RdW4A9k6y/2JVVpK0cOOO0e9XVVv65XuB/frlA4G7Bspt7tdJkqZk4pOx1d2LcMH3I0xyZpKNSTZu3bp10mpIkuYwbtB/a2ZIpv/3vn793cDBA+UO6tc9TVWdX1Vrq2rtqlXzXtglSRrTuFfGXgmcDpzb/3vFwPq3JPkEcBTw8MAQz3bvYxvuHLr+1KNWL3NNJGnxzBv0ST4OHAusTLIZOJsu4C9LcgZwB3ByX/xq4ARgE/AY8PolqLMkaQHmDfqqevUcm44bUraA35i0UpKkxeOVsZLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1LiJgj7Jf0xyS5Kbk3w8yTOSHJpkQ5JNSS5NsvtiVVaStHBjB32SA4G3Amur6nnArsApwHuA91XVYcCDwBmLUVFJ0ngmHbpZAfx4khXAHsAW4KXA5f329cCJEx5DkjSBsYO+qu4G/hi4ky7gHwZuBB6qqsf7YpuBA4c9P8mZSTYm2bh169ZxqyFJmsckQzf7AOuAQ4EDgD2B40d9flWdX1Vrq2rtqlWrxq2GJGkekwzdvAz4f1W1tap+AHwKOAbYux/KATgIuHvCOkqSJjBJ0N8JHJ1kjyQBjgNuBa4DTurLnA5cMVkVJUmTmGSMfgPdSdcvA1/r93U+8A7gN5NsAp4NXLgI9ZQkjWnF/EXmVlVnA2fPWv1N4EWT7FeStHi8MlaSGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDVuxbQrsCP42IY7h64/9ajVy1wTSVo4e/SS1Lhme/Rz9cIlaWdjj16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuImCPsneSS5P8g9Jbkvy4iT7JrkmyTf6f/dZrMpKkhZu0h79ecBfVdXPAM8HbgPOAq6tqsOBa/vHkqQpGTvok+wF/BJwIUBVfb+qHgLWAev7YuuBEyetpCRpfJP06A8FtgJ/nuQrSS5IsiewX1Vt6cvcC+w37MlJzkyyMcnGrVu3TlANSdK2TBL0K4AjgT+rqhcA32HWME1VFVDDnlxV51fV2qpau2rVqgmqIUnalkmCfjOwuao29I8vpwv+byXZH6D/977JqihJmsTYQV9V9wJ3JfnpftVxwK3AlcDp/brTgSsmqqEkaSKTzkf/H4CPJtkd+Cbwero/HpclOQO4Azh5wmNIkiYwUdBX1U3A2iGbjptkv5KkxeOVsZLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1LhJp0DYqX1sw51D15961Oplrokkzc0evSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcd54ZAl4QxJJ2xN79JLUOINekhpn0EtS4xyjX0aO3UuaBnv0ktS4iYM+ya5JvpLkqv7xoUk2JNmU5NIku09eTUnSuBajR/824LaBx+8B3ldVhwEPAmcswjEkSWOaKOiTHAT8a+CC/nGAlwKX90XWAydOcgxJ0mQm7dH/CfA7wI/6x88GHqqqx/vHm4EDJzyGJGkCYwd9kpcD91XVjWM+/8wkG5Ns3Lp167jVkCTNY5Ie/THAK5PcDnyCbsjmPGDvJDNf2zwIuHvYk6vq/KpaW1VrV61aNUE1JEnbMnbQV9V/rqqDqmoNcArwuao6DbgOOKkvdjpwxcS1lCSNbSm+R/8O4DeTbKIbs79wCY4hSRrRolwZW1WfBz7fL38TeNFi7FeSNDmvjJWkxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN88YjOyBvYCJpIezRS1LjDHpJapxBL0mNc4x+OzbXWLwkLYQ9eklqnD367cC0eu7bOq7f4JHaYY9ekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa55WxOwHnzJF2bvboJalxBr0kNc6gl6TGGfSS1DhPxjbEk66ShrFHL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuLGDPsnBSa5LcmuSW5K8rV+/b5Jrknyj/3efxauuJGmhJunRPw78VlUdARwN/EaSI4CzgGur6nDg2v6xJGlKxg76qtpSVV/ulx8FbgMOBNYB6/ti64ETJ62kJGl8izJGn2QN8AJgA7BfVW3pN90L7LcYx5AkjWfiK2OTPBP4JPD2qnokyRPbqqqS1BzPOxM4E2D16tVjH9+rQSVp2ybq0SfZjS7kP1pVn+pXfyvJ/v32/YH7hj23qs6vqrVVtXbVqlWTVEOStA2TfOsmwIXAbVX13oFNVwKn98unA1eMXz1J0qQmGbo5BngN8LUkN/Xrfhc4F7gsyRnAHcDJk1VRkjSJsYO+qq4HMsfm48bdryRpcXllrCQ1zvnoNdRc32Y69ajxvyElaTrs0UtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG+fVKLchCJ5Hz65jS9Nmjl6TG2aPXVHhBlrR87NFLUuPs0WtJeWMYafrs0UtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY1zrhs1yXnzpSfZo5ekxtmj13ZlofPUL/XsmNvav58CtKOwRy9JjbNHrx3CNHvuS30MPxloqdmjl6TGGfSS1DiHbqQpc0hHS80evSQ1bkl69EmOB84DdgUuqKpzl+I40jTt6D3xHb3+Gt2i9+iT7Ap8EPgV4Ajg1UmOWOzjSJJGsxQ9+hcBm6rqmwBJPgGsA25dgmNJ253l+KrmQo670B76OPVf6AVtS/2pYamPu5i/4+X4BLUUY/QHAncNPN7cr5MkTcHUvnWT5EzgzP7ht5N8fVaRlcD9y1ur7Y5tsBO3wWlPLk7UBqfNX2RiCz3GGHValPfBcrTFQi2gTsPa4JBRnrgUQX83cPDA44P6dU9RVecD58+1kyQbq2rt4ldvx2Eb2AZgG4BtAJO1wVIM3XwJODzJoUl2B04BrlyC40iSRrDoPfqqejzJW4D/Tff1yg9X1S2LfRxJ0miWZIy+qq4Grp5wN3MO6+xEbAPbAGwDsA1ggjZIVS1mRSRJ2xmnQJCkxk096JMcn+TrSTYlOWvI9h9Lcmm/fUOSNctfy6U1Qhu8LsnWJDf1P2+YRj2XSpIPJ7kvyc1zbE+S9/ft89UkRy53HZfaCG1wbJKHB94Dv7/cdVxqSQ5Ocl2SW5PckuRtQ8o0/V4YsQ0W/l6oqqn90J2s/b/ATwK7A38PHDGrzL8HPtQvnwJcOs06T6kNXgd8YNp1XcI2+CXgSODmObafAPwlEOBoYMO06zyFNjgWuGra9VziNtgfOLJffhbwf4b8X2j6vTBiGyz4vTDtHv0T0yVU1feBmekSBq0D1vfLlwPHJcky1nGpjdIGTauqLwAPbKPIOuCi6twA7J1k/+Wp3fIYoQ2aV1VbqurL/fKjwG08/ar6pt8LI7bBgk076EeZLuGJMlX1OPAw8Oxlqd3yGHXKiF/rP6penuTgIdtb5rQanRcn+fskf5nkZ6ddmaXUD9G+ANgwa9NO817YRhvAAt8L0w56jeYzwJqq+jngGp78hKOdx5eBQ6rq+cCfAp+ecn2WTJJnAp8E3l5Vj0y7PtMwTxss+L0w7aAfZbqEJ8okWQHsBfzjstRueczbBlX1j1X1vf7hBcAvLFPdthcjTavRsqp6pKq+3S9fDeyWZOWUq7XokuxGF3AfrapPDSnS/HthvjYY570w7aAfZbqEK4HT++WTgM9Vf0aiEfO2wawxyFfSjdvtTK4EXtt/4+Jo4OGq2jLtSi2nJM+ZOTeV5EV0/3db6vDQv74Lgduq6r1zFGv6vTBKG4zzXpjqPWNrjukSkvwBsLGqrqR70Rcn2UR3suqU6dV48Y3YBm9N8krgcbo2eN3UKrwEknyc7psEK5NsBs4GdgOoqg/RXWV9ArAJeAx4/XRqunRGaIOTgDcneRz4LnBKYx0egGOA1wBfS3JTv+53gdWw07wXRmmDBb8XvDJWkho37aEbSdISM+glqXEGvSQ1zqCXpMYZ9JLUOINeT0jyoSTvHLHs51uaRTPJOUku6ZdXJ/l2kl2nXa9p2Nlff4sM+p1IktuTfDfJo0keSvK3Sd6UZBeAqnpTVf3hMtRjUf5I9NO1/qgPpUfTTfU88feqq+rOqnpmVf1w0n0tVLopqX/Yv6aZnw8s8TFvT/KymcfTfP1aGlO9YEpT8Yqq+mySvYB/AZwHHMWOe+HJPVV1UH+l4Drg8iQbqurWaVQmyYp+8r1JfLGqXrIoFZKwR7/TqqqH+6tu/y1wepLnJflIkncBJNknyVXpbnjyYL980KzdPDfJ3yV5JMkVSfad2ZDk6P4Tw0P9LHvH9uv/CPhF4AODvdUkP5PkmiQP9D3zkwf2dUK6GzE8muTuJL895PVUVX0aeBA4Ylt16LcdmuRv+n1eA6wc2LYmSaWbW2mm7Bf6sp9N8sGBYZ6ZsmckuRP43AjH3ivJhUm29K/nXaMMk8z+JNT3/q8feFz9J7Rv9Mf9YP8HcGb7G5Pc1r+OW5McmeRiuqsuP9P/Pn5nyOs/IMmV/e9mU5I3DuzznCSXJbmo3+8tSdbO91q0zKY90b4/y/cD3A68bMj6O4E3Ax8B3tWvezbwa8AedDdA+Avg0wPP+TzdZFLPA/akm4Tpkn7bgXRzb5xA15n45f7xqoHnvmFgX3vSTT37erpPmS8A7qe/4QKwBfjFfnkfnrwxw7HA5n55F+DfAD8AfnqEOnwReC/wY3Q3/Xh0oP5rgAJWDJT9Y7obw7wEeGRI2Yv61/HjIxz7fwH/sy//z4C/A/5dv+11wPVz/P5mt9tTyvb1uArYmy68twLH99te1f++Xkh3047D6GZAhFnviyGv/wvA/wCeAfx8v9+X9tvOAf6pf627Au8Gbpj2e92fp/7YoxfAPcC+gyuqmzHzk1X1WHU3QPgjuqGeQRdX1c1V9R3gncDJfc/014Grq+rqqvpRVV0DbKQLg2FeDtxeVX9eVY9X1Vfo/nC8qt/+A+CIJD9RVQ9Wf2OG3gFJHqL7w3A28Jqq+vq26pBkNV3gvbOqvlfdTT8+M6xiA2V/v6q+X1XX8/SJ9wDOqarvVNV35zn2fn07vL0vfx/wPp46h9PRfY985ufoOdptmHOr6qGquhO4ji6YAd4A/Leq+lJ1NlXVHfPtLN29D44B3lFV/1RVN9HNoPragWLX96/1h8DFwPMXUF8tA8foBV0P9Cl3N0qyB10AHU/XiwZ4VpJd68mTdIM3gLiDbhKulcAhwKuSvGJg+250wTPMIcBRfWDPWEEXGtB9svg94NwkXwXOqqov9tvuqarZQ0oz+5yrDgcAD/Z/oAbrP+yGLgcAD1TVYwPr7hpSdrAttnXsQ/rlLQOjKrvMev4NNf4Y/b0Dy48Bz+yXD6a7ZeVCzbz+RwfW3QEMDs/MPuYzFulchRaJQb+TS/JCuqC/nu6k7IzfohsCOaqq7k3y88BX6D72zxgMu9V0Pe/76ULr4qp6I8PNnknvLuBvquqXhxau+hKwLt083W8BLmN4KM/e59A6JDkE2CfJngNhv3pIvaAbNto3yR4DYT/s2IPP3dax9we+B6wcIwi/QzeUNuM5C3juXcBz59i2rZkN76F7/c8aCPvVNDYHfOscutlJJfmJJC+nu0ftJVX1tVlFnkU3BepD/UnWs4fs5teTHNH3/v8AuLzv7V8CvCLJv0qya5JnpPsq5EzP+1t0N0OfcRXwU0lek2S3/ueFSf55kt2TnJZkr6r6Ad34+I9GeIlz1qEfstgI/Nd+/y8BXjFsJwNlz+nLvniusiMeewvw18B/738HuyR5bpLZw2LD3AT8apI9khwGnDHCc2ZcAPx2kl9I57D+Dx48/ffxhKq6C/hb4N396/i5/riXLODYmjKDfufzmSSP0vXw/gvdCclhX638E7oTi/cDNwB/NaTMxXQncO+lO1H3VngiHNbRzaO9tT/Wf+LJ99t5wEnpvs3z/r6n+C/pxqnv6ff3HroTpdDNz317kkeANwGnzfciR6jDqXSfYB6g+yN20TZ2dxrwYroTqu8CLqXrlY977NfSndi9le5bQpcDo9zg+n3A9+mCeT3w0RGeM1Onv6A7z/IxuhPPn+bJ8zLvBn6vPx/wtG80Aa+mO0F7D92J5LOr6rOjHlvT53z00gIluRT4h6oa9ilH2u7Yo5fm0Q8jPbcfZjmerrfe7M251R5Pxkrzew7wKbprCzYDb+6/AirtEBy6kaTGOXQjSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGvf/AbQIFlmS3x/YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.DiabetesPedigreeFunction.values, bins=50, kde=False)\n",
    "plt.xlabel('DiabetesPedigreeFunction', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.Age.values, bins=50, kde=False)\n",
    "plt.xlabel('Age', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 缺失值\n",
    "## 按列统计\n",
    "有缺失值的列有Glucose、BloodPressure、SkinThickness、Insulin、BMI五列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Glucose': 0.6510416666666666, 'BloodPressure': 4.557291666666667, 'SkinThickness': 29.557291666666668, 'Insulin': 48.697916666666664, 'BMI': 1.4322916666666667}\n"
     ]
    }
   ],
   "source": [
    "# 缺失值\n",
    "incmpCol = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']\n",
    "incmpRatio = {}\n",
    "for col in incmpCol:\n",
    "    incmpRatio[col] = 100 * (data[abs(data[col]) < 1e-6].shape[0])/data.shape[0]\n",
    "\n",
    "# 缺失率\n",
    "print(incmpRatio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征间的关联性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 9)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('Diabetes.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies and Age = 0.54\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值填补\n",
    "有缺失值的特征有['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']，缺失率分别为 0.65%, 4.56%,  29.56%, 48.70%, 1.43%。均采用中位数填补。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zhangwt/.local/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>733.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>757.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>121.686763</td>\n",
       "      <td>72.405184</td>\n",
       "      <td>29.153420</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>32.457464</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>30.535641</td>\n",
       "      <td>12.382158</td>\n",
       "      <td>10.476982</td>\n",
       "      <td>118.775855</td>\n",
       "      <td>6.924988</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>76.250000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  763.000000     733.000000     541.000000  394.000000   \n",
       "mean      3.845052  121.686763      72.405184      29.153420  155.548223   \n",
       "std       3.369578   30.535641      12.382158      10.476982  118.775855   \n",
       "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
       "25%       1.000000   99.000000      64.000000      22.000000   76.250000   \n",
       "50%       3.000000  117.000000      72.000000      29.000000  125.000000   \n",
       "75%       6.000000  141.000000      80.000000      36.000000  190.000000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  757.000000                768.000000  768.000000  768.000000  \n",
       "mean    32.457464                  0.471876   33.240885    0.348958  \n",
       "std      6.924988                  0.331329   11.760232    0.476951  \n",
       "min     18.200000                  0.078000   21.000000    0.000000  \n",
       "25%     27.500000                  0.243750   24.000000    0.000000  \n",
       "50%     32.300000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将缺失值设为NaN\n",
    "cols = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']\n",
    "for col in cols:\n",
    "    #data[col].apply(lambda x: np.NaN if abs(x) < 1e-6 )\n",
    "    data[col][data[col] < 1e-6] = np.nan\n",
    "    \n",
    "data.to_csv('nan.csv')\n",
    "    \n",
    "data.describe()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imp = Imputer(missing_values='NaN', strategy='median',axis=0)\n",
    "dataFE = pd.DataFrame(imp.fit_transform(data), columns=data.columns)\n",
    "dataFE.to_csv('dataFE.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>121.656250</td>\n",
       "      <td>72.386719</td>\n",
       "      <td>29.108073</td>\n",
       "      <td>140.671875</td>\n",
       "      <td>32.455208</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>30.438286</td>\n",
       "      <td>12.096642</td>\n",
       "      <td>8.791221</td>\n",
       "      <td>86.383060</td>\n",
       "      <td>6.875177</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  121.656250      72.386719      29.108073  140.671875   \n",
       "std       3.369578   30.438286      12.096642       8.791221   86.383060   \n",
       "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
       "25%       1.000000   99.750000      64.000000      25.000000  121.500000   \n",
       "50%       3.000000  117.000000      72.000000      29.000000  125.000000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    32.455208                  0.471876   33.240885    0.348958  \n",
       "std      6.875177                  0.331329   11.760232    0.476951  \n",
       "min     18.200000                  0.078000   21.000000    0.000000  \n",
       "25%     27.500000                  0.243750   24.000000    0.000000  \n",
       "50%     32.300000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFE.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0          6.0    148.0           72.0           35.0    125.0  33.6   \n",
       "1          1.0     85.0           66.0           29.0    125.0  26.6   \n",
       "2          8.0    183.0           64.0           29.0    125.0  23.3   \n",
       "3          1.0     89.0           66.0           23.0     94.0  28.1   \n",
       "4          0.0    137.0           40.0           35.0    168.0  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction   Age  Outcome  \n",
       "0                     0.627  50.0      1.0  \n",
       "1                     0.351  31.0      0.0  \n",
       "2                     0.672  32.0      1.0  \n",
       "3                     0.167  21.0      0.0  \n",
       "4                     2.288  33.0      1.0  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFE.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
